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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pick Best Model Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install -U pip\n",
    "\n",
    "# If you don't have ClearML installed then uncomment this line\n",
    "# ! pip install -U clearml==0.16.2rc0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from clearml import Task, OutputModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure Task\n",
    "Instantiate a ClearML Task using `Task.init`. \n",
    "\n",
    "A Configuration dictionary is connected to the task using `Task.connect`. This will enable the pipeline controller to access this task's configurations and override the values when the pipeline is executed.\n",
    "\n",
    "Notice in the [pipeline controller script](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/tabular_ml_pipeline.ipynb) that when this task is added as a step in the pipeline, the value of `train_task_ids` is overridden. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = Task.init(project_name=\"Tabular Example\", task_name=\"pick best model\")\n",
    "configuration_dict = {\n",
    "    \"train_tasks_ids\": [\n",
    "        \"c9bff3d15309487a9e5aaa00358ff091\",\n",
    "        \"c9bff3d15309487a9e5aaa00358ff091\",\n",
    "    ]\n",
    "}\n",
    "configuration_dict = task.connect(\n",
    "    configuration_dict\n",
    ")  # enabling configuration override by clearml\n",
    "print(\n",
    "    configuration_dict\n",
    ")  # printing actual configuration (after override in remote mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Models\n",
    "The task retrieves the IDs of the training tasks from the configuration dictionary. Then each training task's last scalar metrics are retrieved and compared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}\n",
    "for task_id in configuration_dict.get(\"train_tasks_ids\"):\n",
    "    train_task = Task.get_task(task_id)\n",
    "    results[task_id] = train_task.get_last_scalar_metrics()[\"accuracy\"][\"total\"][\"last\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model_task_id = max(results.items(), key=lambda x: x[1])[0]\n",
    "best_model_id = Task.get_task(best_model_task_id).output_model_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "OutputModel(base_model_id=best_model_id)"
   ]
  }
 ],
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